Generative AI has the potential to revolutionize cybersecurity by utilizing artificial intelligence techniques to generate new data [3]. This can greatly enhance threat detection and prevention, including identifying malware, phishing attempts [3], and data breaches [3]. By analyzing patterns and behaviors in large amounts of data [3], generative AI can provide valuable insights and improve overall cybersecurity strategies.


Generative AI offers numerous practical use cases in cybersecurity. These include information management [3], malware analysis [3], tool development [3], risk evaluation [3], tabletop exercises [3], incident response [2] [3], and threat intelligence [3]. With generative AI [1] [2] [3], organizations can distill and summarize large amounts of security-related information [3], detect anomalies in malware code [3], create useful tools [3], evaluate risk scenarios from different perspectives [3], generate customized tabletop scenarios [3], expedite incident response [3], and enhance threat intelligence tasks [3].

Generative AI tools have been utilized in both positive and negative ways in cybersecurity [1]. Positive impacts include advanced threat detection [2], automated incident response [2], adaptive security policies [2], and human augmentation [2]. However, there are also negative impacts to consider, such as adversarial attacks, data poisoning [2], over-reliance on automation [2], and ethical concerns [2].

To effectively implement generative AI in cybersecurity [2], organizations should follow relevant frameworks such as ISO/IEC 27032 [2]. This international standard provides guidelines for cybersecurity risk management [2], emphasizing risk assessment [2], security policies [2], and incident management [2]. By implementing generative AI responsibly and securely [2], organizations can mitigate potential risks and ensure the effectiveness of their cybersecurity strategies.


Generative AI has had a significant impact on the cybersecurity landscape [1]. Its potential to revolutionize threat detection and prevention is evident, but it also brings challenges that need to be addressed. Organizations must be aware of the positive and negative impacts of generative AI and take appropriate measures to mitigate risks. By following established frameworks like ISO/IEC 27032 [2], organizations can effectively implement generative AI in their cybersecurity strategies and stay ahead in the ever-evolving landscape of cybersecurity.